GPS Movement Segmentation for Workforce Visualization
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Solution Overview
Problem
Conventional systems fail to effectively analyze and visualize movement and idle segments of GPS-tracked individuals or objects, lacking the ability to depict the corresponding path and appointment locations, and there is a need for a reliable method to identify and map movement segments, including time and duration, for improved workforce management and customer service.
Innovation Solution
A system and method that utilizes GPS signal data from smartphones and tablets to produce movement and idle segments, transforming the data into time-on-site indicator data, and visualizing the type, path, and location of GPS-enabled task persons and equipment, enabling better workforce management and customer interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional systems report raw GPS location data without analysis, then the system complexity is low, but the ability to identify movement segments and provide actionable insights is insufficient
Solution Approach 1:
The patent segments continuous GPS trajectory data into discrete movement segments with distinct characteristics (moving, idle, stopped). Each segment is identified by analyzing changes in location, time, and velocity parameters, allowing the system to transform raw continuous data into structured actionable information without excessive complexity
Solution Approach 2:
The patent introduces an intermediary processing layer that acts as a mediator between raw GPS data collection and final visualization. This layer includes algorithms that analyze location sequences, detect movement patterns, and generate structured movement segment data, thereby preserving information while managing processing complexity through modular architecture
2Productivity
If the system tracks and visualizes detailed movement segments with path and location information, then workforce productivity and customer service quality improve, but the data processing and visualization complexity increases
Solution Approach 1:
The system segments workforce activities into distinct movement segments (moving, idle, stopped) that can be individually analyzed and visualized. This segmentation enables productivity measurements for different activity types while keeping the underlying processing manageable through consistent segmentation logic applied across all workers
Solution Approach 2:
The patent adds temporal and contextual dimensions to GPS location data by incorporating time stamps, duration metrics, and movement state classification. This transforms simple location coordinates into rich movement segment records that provide actionable productivity insights without requiring proportional increases in system complexity
3Measurement precision
If the system analyzes time stamp location data to produce movement and idle segments, then the accuracy of workforce monitoring improves, but the processing time and computational resources increase
Solution Approach 1:
The system processes GPS data in segmented sequences rather than analyzing all data points simultaneously. By dividing the trajectory into discrete movement segments and applying detection algorithms to each segment independently, the system achieves high measurement precision while reducing overall processing time through incremental analysis
Solution Approach 2:
The patent applies partial action by focusing computational resources on detecting and analyzing only the segments that contain meaningful movement patterns. The system identifies key transition points in the GPS data and concentrates processing effort on these critical segments rather than uniformly processing all data points, thereby improving accuracy where needed while minimizing unnecessary processing
Data Source
AI summary
A GPS signal is converted into motion and idle indicator data. A task database includes task situs-location data, assignment and person data. Determining an idle mode generates idle ON indicia. Determining a movement ON status generates movement tracking indicia with location and time-based tracking over a subject time period. A map over a geographic region encompasses the idle and movement locations and overlays, on proximal locations on the map, the idle mode ON indicia and the movement tracking indicia. The marked map is published. Enhancements define the subject time period as the present plus a predetermined time; a historic period; a selectable time; and sequential historic time. Further, the subject time period is selectable over the location or path of the movement tracking indicia. A privacy event masks the publication of idle and movement indicia during a privacy block.